Retirado do artigo Miller et al. (2019). Distance sampling in R. Journal of Statistical Sofware 89(1)
dados_completos |>
group_by(
uc_name,
ea_name
) |>
filter(day_effort == max(day_effort)) |>
drop_na(distance) |>
ungroup()
dados_completos |>
filter(
uc_name == "Resex Tapajos-Arapiuns",
sp_name == "Dasyprocta croconota"
) |>
datatable(filter = list(position = "top"))
Variáveis necessárias para o data.frame:
Region.Label: vetor fator com o estrato contendo o
transecto (pode ser uma estratificação pré-amostragem - UCs - ou
pós-amostragem - ex. região, estado, bioma)
Area: vetor numérico contendo a área do
estrato;
Sample.Label: vetor númerico contendo a identidade
(ID) do transecto
object: nome adicional, ver seção 6;
detected: nome adicional, ver seção 6;
Effort: vetor númerico contendo o esforço do
transecto (para linhas seu comprimento, para pontos o número de vezes
que o ponto foi visitado)
size: vetor numérico copntendo o tamanho do grupo
observado;
distance: vetor numérico de distâncias
observadas;
Month:
OBs:
Sp:
mas:
HAS:
Study.Area:
Transectos que foram amostrados, mas que não tiveram observações (n =
0) devem ser incluídos no conjunto de dados com NA nas
observações de distância e qualquer outra covariael para a qual não se
tenha observação.
# cutia_tap_arap |>
# complete(Region.Label, Sample.Label, sp_name) |>
# datatable(filter = list(position = "top"))
Jogar a imputacao de NAs pra dentro da funcao carregar
dados completos.
# desenha o grafico com a distribuicao de distancias perpendiculares
cutia_tap_arap |>
filter(distance >= 1,
distance <= 14) |>
plotar_distribuicao_distancia_interativo()
summary(cutia_tap_arap$distance)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 1.550 6.000 7.187 10.000 50.000
Ajustando um modelo ao dados das cutias Dasyprocta
croconota, configurando uma distância limite de 20m e usando
Half-normal como key function usando o argumento
key, sem termo de ajuste.
# Key function - Half-normal
cutia_tap_arap_hn <- purrr::map(
dist_truncamento,
\(.x) ajuste_modelos_distance_hn(
cutia_tap_arap_filtrado,
truncamento = .x
)
)
Fitting half-normal key function
AIC= 4346.405
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 4346.405
Fitting half-normal key function with cosine(2) adjustments
AIC= 4338.754
Fitting half-normal key function with cosine(2,3) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4328.058
Fitting half-normal key function with cosine(2,3,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4322.361
Fitting half-normal key function with cosine(2,3,4,5) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4316.235
Fitting half-normal key function with cosine(2,3,4,5,6) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4361.776
Half-normal key function with cosine(2,3,4,5) adjustments selected.
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 4346.405
Fitting half-normal key function with Hermite(4) adjustments
AIC= 4331.217
Fitting half-normal key function with Hermite(4,6) adjustments
AIC= 4333.267
Half-normal key function with Hermite(4) adjustments selected.
Fitting half-normal key function
AIC= 3893.364
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 3893.364
Fitting half-normal key function with cosine(2) adjustments
Warning: First partial hessian is singular; using second-partial hessian
Warning: Detection function is not weakly monotonic!Warning: Detection function is not strictly monotonic!Warning: Detection function is greater than 1 at some distancesWarning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Half-normal key function selected.
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 3893.364
Fitting half-normal key function with Hermite(4) adjustments
Warning: First partial hessian is singular; using second-partial hessian
AIC= 3895.364
Half-normal key function selected.
Fitting half-normal key function
AIC= 3262.461
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 3262.461
Fitting half-normal key function with cosine(2) adjustments
Warning: First partial hessian is singular; using second-partial hessian
Warning: Detection function is not weakly monotonic!Warning: Detection function is not strictly monotonic!Warning: Detection function is greater than 1 at some distancesWarning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Half-normal key function selected.
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 3262.461
Fitting half-normal key function with Hermite(4) adjustments
AIC= 3264.461
Half-normal key function selected.
Fitting half-normal key function
AIC= 1803.913
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 1803.913
Fitting half-normal key function with cosine(2) adjustments
Warning: First partial hessian is singular; using second-partial hessian
Warning: Detection function is not weakly monotonic!Warning: Detection function is not strictly monotonic!Warning: Detection function is greater than 1 at some distancesWarning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Half-normal key function selected.
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 1803.913
Fitting half-normal key function with Hermite(4) adjustments
Warning: First partial hessian is singular; using second-partial hessian
AIC= 1899.845
Half-normal key function selected.
cutia_tap_arap_hn
$`20 metros`
$`20 metros`$`Sem termo`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 2310.909
$`20 metros`$Cosseno
Distance sampling analysis object
Detection function:
Half-normal key function with cosine adjustment terms of order 2,3,4,5
Estimated abundance in covered region: 3280.45
$`20 metros`$`Hermite polinomial`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 2310.909
$`15 metros`
$`15 metros`$`Sem termo`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 1767.642
$`15 metros`$Cosseno
Distance sampling analysis object
Detection function:
Half-normal key function with cosine adjustment terms of order 2,3
Estimated abundance in covered region: 2320.64
$`15 metros`$`Hermite polinomial`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 1767.642
$`10 metros`
$`10 metros`$`Sem termo`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 1154.866
$`10 metros`$Cosseno
Distance sampling analysis object
Detection function:
Half-normal key function with cosine adjustment terms of order 2,3,4
Estimated abundance in covered region: 2010.188
$`10 metros`$`Hermite polinomial`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 1154.866
$`5 metros`
$`5 metros`$`Sem termo`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 806.2075
$`5 metros`$Cosseno
Distance sampling analysis object
Detection function:
Half-normal key function with cosine adjustment terms of order 2,3,4,5
Estimated abundance in covered region: 1892.596
$`5 metros`$`Hermite polinomial`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 806.2075
Ajustando um modelo ao dados da cutia Dasyprocta croconota,
configurando uma distância limite de 20m e usando Hazard rate
como key function usando o argumento key.
# ajustando a função de detecção para uma distancia de truncamento de 20, 15, 10 e 5 metros
# Key function - Hazard-rate
cutia_tap_arap_hr <- purrr::map(
dist_truncamento,
\(.x) ajuste_modelos_distance_hr(
cutia_tap_arap_filtrado,
truncamento = .x
)
)
Fitting hazard-rate key function
AIC= 4302.674
Starting AIC adjustment term selection.
Fitting hazard-rate key function
AIC= 4302.674
Fitting hazard-rate key function with cosine(2) adjustments
AIC= 4304.745
Hazard-rate key function selected.
Starting AIC adjustment term selection.
Fitting hazard-rate key function
AIC= 4302.674
Fitting hazard-rate key function with simple polynomial(4) adjustments
AIC= 4304.726
Hazard-rate key function selected.
Fitting hazard-rate key function
Warning: First partial hessian is singular; using second-partial hessian
AIC= 3892.732
Starting AIC adjustment term selection.
Fitting hazard-rate key function
Warning: First partial hessian is singular; using second-partial hessian
AIC= 3892.732
Fitting hazard-rate key function with cosine(2) adjustments
Warning: First partial hessian is singular; using second-partial hessian
Warning: Detection function is not weakly monotonic!Warning: Detection function is not strictly monotonic!Warning: Detection function is greater than 1 at some distancesWarning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Hazard-rate key function selected.
Starting AIC adjustment term selection.
Fitting hazard-rate key function
Warning: First partial hessian is singular; using second-partial hessian
AIC= 3892.732
Fitting hazard-rate key function with simple polynomial(4) adjustments
Warning: First partial hessian is singular; using second-partial hessian
AIC= 3897.364
Hazard-rate key function selected.
Fitting hazard-rate key function
AIC= 3262.081
Starting AIC adjustment term selection.
Fitting hazard-rate key function
AIC= 3262.081
Fitting hazard-rate key function with cosine(2) adjustments
Warning: First partial hessian is singular; using second-partial hessian
Warning: Detection function is not weakly monotonic!Warning: Detection function is not strictly monotonic!Warning: Detection function is greater than 1 at some distancesWarning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Hazard-rate key function selected.
Starting AIC adjustment term selection.
Fitting hazard-rate key function
AIC= 3262.081
Fitting hazard-rate key function with simple polynomial(4) adjustments
Warning: First partial hessian is singular; using second-partial hessian
AIC= 3266.461
Hazard-rate key function selected.
Fitting hazard-rate key function
Warning: First partial hessian is singular; using second-partial hessian
AIC= 1804.357
Starting AIC adjustment term selection.
Fitting hazard-rate key function
Warning: First partial hessian is singular; using second-partial hessian
AIC= 1804.357
Fitting hazard-rate key function with cosine(2) adjustments
Warning: First partial hessian is singular; using second-partial hessian
Warning: Detection function is not weakly monotonic!Warning: Detection function is not strictly monotonic!Warning: Detection function is greater than 1 at some distancesWarning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Hazard-rate key function selected.
Starting AIC adjustment term selection.
Fitting hazard-rate key function
Warning: First partial hessian is singular; using second-partial hessian
AIC= 1804.357
Fitting hazard-rate key function with simple polynomial(4) adjustments
Warning: First partial hessian is singular; using second-partial hessian
AIC= 1807.913
Hazard-rate key function selected.
Ajustando um modelo ao dados das cutias Dasyprocta
croconota, configurando uma distância limite de 20m e usando
Uniform como key function usando o argumento
key, sem termo de ajuste.
# ajustando a função de detecção para uma distancia de truncamento de 20, 15, 10 e 5 metros
# Key function - Uniform
cutia_tap_arap_unif <- purrr::map(
dist_truncamento,
\(.x) ajuste_modelos_distance_unif(
cutia_tap_arap,
truncamento = .x
)
)
Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 5995.938
Fitting uniform key function with cosine(1) adjustments
AIC= 5796.939
Fitting uniform key function with cosine(1,2) adjustments
AIC= 5796.796
Fitting uniform key function with cosine(1,2,3) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5729.113
Fitting uniform key function with cosine(1,2,3,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5729.808
Uniform key function with cosine(1,2,3) adjustments selected.
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 5995.938
Fitting uniform key function with simple polynomial(2) adjustments
AIC= 5799.151
Fitting uniform key function with simple polynomial(2,4) adjustments
AIC= 5796.184
Fitting uniform key function with simple polynomial(2,4,6) adjustments
AIC= 5791.29
Fitting uniform key function with simple polynomial(2,4,6,8) adjustments
AIC= 5788.217
Fitting uniform key function with simple polynomial(2,4,6,8,10) adjustments
AIC= 5783.105
Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 5432.006
Fitting uniform key function with cosine(1) adjustments
AIC= 5340.635
Fitting uniform key function with cosine(1,2) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5319.03
Fitting uniform key function with cosine(1,2,3) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5282.176
Fitting uniform key function with cosine(1,2,3,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5271.725
Fitting uniform key function with cosine(1,2,3,4,5) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5213.714
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 5432.006
Fitting uniform key function with simple polynomial(2) adjustments
AIC= 5366.091
Fitting uniform key function with simple polynomial(2,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5346.537
Fitting uniform key function with simple polynomial(2,4,6) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5338.476
Fitting uniform key function with simple polynomial(2,4,6,8) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5332.686
Fitting uniform key function with simple polynomial(2,4,6,8,10) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5326.193
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 4688.063
Fitting uniform key function with cosine(1) adjustments
AIC= 4647.342
Fitting uniform key function with cosine(1,2) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4617.841
Fitting uniform key function with cosine(1,2,3) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4579.748
Fitting uniform key function with cosine(1,2,3,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4551.3
Fitting uniform key function with cosine(1,2,3,4,5) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4523.924
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 4688.063
Fitting uniform key function with simple polynomial(2) adjustments
AIC= 4680.313
Fitting uniform key function with simple polynomial(2,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4651.715
Fitting uniform key function with simple polynomial(2,4,6) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4637.792
Fitting uniform key function with simple polynomial(2,4,6,8) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4631.554
Fitting uniform key function with simple polynomial(2,4,6,8,10) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4622.947
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 3008.377
Fitting uniform key function with cosine(1) adjustments
AIC= 2949.701
Fitting uniform key function with cosine(1,2) adjustments
Warning: Detection function is not strictly monotonic!AIC= 2910.157
Fitting uniform key function with cosine(1,2,3) adjustments
AIC= 2845.858
Fitting uniform key function with cosine(1,2,3,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 2801.709
Fitting uniform key function with cosine(1,2,3,4,5) adjustments
Warning: Detection function is not strictly monotonic!AIC= 2744.89
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 3008.377
Fitting uniform key function with simple polynomial(2) adjustments
AIC= 2981.459
Fitting uniform key function with simple polynomial(2,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 2956.964
Fitting uniform key function with simple polynomial(2,4,6) adjustments
Warning: Detection function is not strictly monotonic!AIC= 2942.061
Fitting uniform key function with simple polynomial(2,4,6,8) adjustments
Warning: Detection function is not strictly monotonic!AIC= 2929.72
Fitting uniform key function with simple polynomial(2,4,6,8,10) adjustments
Warning: Detection function is not strictly monotonic!AIC= 2917.584
Warning: Detection function is not strictly monotonic!
summarize_ds_models(
cutia_tap_arap_hn$`14 metros`$`Sem termo`,
cutia_tap_arap_hn$`14 metros`$Cosseno,
cutia_tap_arap_hn$`14 metros`$`Hermite polinomial`,
cutia_tap_arap_hr$`14 metros`$`Sem termo`,
cutia_tap_arap_hr$`14 metros`$Cosseno,
cutia_tap_arap_hr$`14 metros`$`Polinomial simples`,
cutia_tap_arap_unif$`14 metros`$Cosseno,
cutia_tap_arap_unif$`14 metros`$`Polinomial simples`
)
summarize_ds_models(
cutia_tap_arap_hn$`12 metros`$`Sem termo`,
cutia_tap_arap_hn$`12 metros`$Cosseno,
cutia_tap_arap_hn$`12 metros`$`Hermite polinomial`,
cutia_tap_arap_hr$`12 metros`$`Sem termo`,
cutia_tap_arap_hr$`12 metros`$Cosseno,
cutia_tap_arap_hr$`12 metros`$`Polinomial simples`,
cutia_tap_arap_unif$`12 metros`$Cosseno,
cutia_tap_arap_unif$`12 metros`$`Polinomial simples`
)
summarize_ds_models(
cutia_tap_arap_hn$`10 metros`$`Sem termo`,
cutia_tap_arap_hn$`10 metros`$Cosseno,
cutia_tap_arap_hn$`10 metros`$`Hermite polinomial`,
cutia_tap_arap_hr$`10 metros`$`Sem termo`,
cutia_tap_arap_hr$`10 metros`$Cosseno,
cutia_tap_arap_hr$`10 metros`$`Polinomial simples`,
cutia_tap_arap_unif$`10 metros`$Cosseno,
cutia_tap_arap_unif$`10 metros`$`Polinomial simples`
)
summarize_ds_models(
cutia_tap_arap_hn$`7 metros`$`Sem termo`,
cutia_tap_arap_hn$`7 metros`$Cosseno,
cutia_tap_arap_hn$`7 metros`$`Hermite polinomial`,
cutia_tap_arap_hr$`7 metros`$`Sem termo`,
cutia_tap_arap_hr$`7 metros`$Cosseno,
cutia_tap_arap_hr$`7 metros`$`Polinomial simples`,
cutia_tap_arap_unif$`7 metros`$Cosseno,
cutia_tap_arap_unif$`7 metros`$`Polinomial simples`
)
O que tem que ter?
Os gráficos (probabilidade de detecção pela distância, com a curva ajustada, exemplo abaixo, fazer no ggplot), resultado do goodness of fit (gof_ds()), cada modelo vai ter que ter um nome diferente numa tabela(?)
plot(cutia_tap_arap_hn, breaks = seq(0, 20, 2.5))
plot(cutia_tap_arap_hn_herm, breaks = seq(0, 20, 2.5))
plot(cutia_tap_arap_hn_cos, breaks = seq(0, 20, 2.5))
plot(cutia_tap_arap_hr, breaks = seq(0, 20, 2.5))
plot(cutia_tap_arap_hr_poly, breaks = seq(0, 20, 2.5))
plot(cutia_tap_arap_hr_cos, breaks = seq(0, 20, 2.5))
Podemos usar a função summary para obter informações
importantes sobre o modelo.
lista_modelos <- list(
cutia_tap_arap_hn,
cutia_tap_arap_hn_herm,
cutia_tap_arap_hn_cos,
cutia_tap_arap_hr,
cutia_tap_arap_hr_poly,
cutia_tap_arap_hr_cos
)
purrr::map(lista_modelos, \(x) summary(x))
[[1]]
Summary for distance analysis
Number of observations : 1277
Distance range : 0 - 20
Model : Half-normal key function
AIC : 7212.428
Detection function parameters
Scale coefficient(s):
NA
Summary statistics:
Density:
[[2]]
Summary for distance analysis
Number of observations : 1277
Distance range : 0 - 20
Model : Half-normal key function
AIC : 7212.428
Detection function parameters
Scale coefficient(s):
NA
Summary statistics:
Density:
[[3]]
Summary for distance analysis
Number of observations : 1277
Distance range : 0 - 20
Model : Half-normal key function with cosine adjustment terms of order 2,3,4,5
Strict monotonicity constraints were enforced.
AIC : 7130.51
Detection function parameters
Scale coefficient(s):
Adjustment term coefficient(s):
NA
Summary statistics:
Density:
[[4]]
Summary for distance analysis
Number of observations : 1277
Distance range : 0 - 20
Model : Hazard-rate key function
AIC : 6888.167
Detection function parameters
Scale coefficient(s):
Shape coefficient(s):
NA
Summary statistics:
Density:
[[5]]
Summary for distance analysis
Number of observations : 1277
Distance range : 0 - 20
Model : Hazard-rate key function
AIC : 6888.167
Detection function parameters
Scale coefficient(s):
Shape coefficient(s):
NA
Summary statistics:
Density:
[[6]]
Summary for distance analysis
Number of observations : 1277
Distance range : 0 - 20
Model : Hazard-rate key function
AIC : 6888.167
Detection function parameters
Scale coefficient(s):
Shape coefficient(s):
NA
Summary statistics:
Density:
NA
summarize_ds_models(
cutia_tap_arap_hn,
cutia_tap_arap_hn_herm,
cutia_tap_arap_hn_cos,
cutia_tap_arap_hr,
cutia_tap_arap_hr_poly,
cutia_tap_arap_hr_cos
)
O resultado inclui detalhes sobre o dado e a especificação do modelo, assim como dos coeficientes (\(\beta_{j}\)) e sua inceteza, a média do valor de detectabilidade e sua incerteza e uma estimativa da abundância na área coberta pela amostragem (sem levar em consideração o tamanho dos agrupamentos, ou bandos).
Para visualizar quão bem a função de detecção se ajusta aos dados quanto temos as distâncias exatas podemos usar um plot de quantis empíricos x teóricos (Q-Q plot). Ele compara a função de distribuição cumulativa (CDF) dos valores ajustados da função detecção a distribuição empírica dos dados (EDF).
Também podemos usar o teste de Cramér-von Mises para testar se os pontos da EDF e da CDF tem origem na mesma distribuição. O teste usa a soma de todas as distâncias entre um ponto e a linha y = x para formar a estatística a ser testada. Um resultado significativo fornece evidência contra a hiipótese nula, sugerindo que o modelo não se ajusta bem aos dados.
# ajustando um modelo Half-normal
cutia_hn <- ds(data = cutia_tap_arap_15,
truncation = 20,
transect = "line",
key = "hn",
adjustment = NULL)
# conduzindo o teste dfe bondadede ajuste de Cramer-von Mises
gof_ds(cutia_hn)
gof_ds(cutia_hr_time)
O resutlado do teste aponta que o modelo Half-normal deve ser descartado.
Testes de bondade de ajuste de chi-quadrado são gerados usando a
função gof_ds quando as distâncias forneceidas estão
categorizadas.
Uma vez que temos um conjunto de modelos plausíveis, podemos utilizar
o cirtériode informaçãode Akaike (AIC) para selecionar entre os modelos
o que melhor se ajusta aos dados utilizando a função
summarize_ds_models.
# ajustando a função de detecção para uma distancia de truncamento de 20 metros
# Key function - Half-normal
cutia_tap_arap_hn_herm <- cutia_tap_arap |>
ds(
truncation = 10,
key = "hn",
adjustment = "herm"
)
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 4676.418
Fitting half-normal key function with Hermite(4) adjustments
AIC= 4677.111
Half-normal key function selected.
O melhor modelo é o Hazard-rate com tempo de senso e tamanho do grupo como covariáveis.
Para obter a abundância na região de estudo, primeiro calculamos a abundância na área amostrada para obter \(N_c\) e em seguida escalonamos esse valor para toda a área de estudo multiplicando \(N_c\) pela razão entre a área amostrada e a área da região. Para estimar a abundância na área amostrada, utilizamos as estimativas de probabilidade de detecção no estimador de Horvitz-Thompson.
Quando fornecemos os dados no formato correto (“flatfile”)
ds irá automaticamente calcular as estimativas de
abundância baseado nas informações de amostragem presenta nos dados.
# ajustando a função de detecção para uma distancia de truncamento de 20 metros
# Key function - Half-normal
cutia_tap_arap_hn_cos <- cutia_tap_arap |>
ds(
truncation = 10,
key = "hn"
)
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 4676.418
Fitting half-normal key function with cosine(2) adjustments
AIC= 4616.112
Fitting half-normal key function with cosine(2,3) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4606.713
Fitting half-normal key function with cosine(2,3,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4538.956
Fitting half-normal key function with cosine(2,3,4,5) adjustments
Warning: Detection function is not strictly monotonic!AIC= 6262.112
Half-normal key function with cosine(2,3,4) adjustments selected.
Warning: Detection function is not strictly monotonic!
Summary statistics: fornece as áreas, aŕea de amostragem, esforço, número de observações, número de transectos, taxa de encontro, seus erros padrões e coeficientes de variação para cada estrato;
Abundance: fornece estimativas, erros padrões, coeficientesde variação, intervalos de confiança inferior e superior, graus de liberdade para a estimativa de abundância de cada estrato;
Densidade: lista as mesmas estatísticas de Abundance, só que para densidade.
contar_n_repeticoes_trilha() - conta o número de vezes
que cada trilha foi visitada
Ajuste Hermite pollynomial usa od código "herm"
e polinomial simples "poly".
Podemos incluir covariáveis utilizando o argumento
formula = ~ .... Abaixo, está especificado um modelo
“Hazard-rate” para os dados de cutia q ue inclui o tempo de senso como
covariável e uma distância limite de 20m.
cutia_hr_time <- cutia_tap_arap_15 |>
ds(truncation = 20,
key = "hr",
formula = ~ cense_time)
Adicionando uma segunda covariável: tamanho do grupo.
cutia_hr_time_size <- ds(data = cutia_tap_arap_15,
truncation = 20,
transect = "line",
key = "hr",
formula = ~ cense_time + size)
plot(cutia_hr_time)
plot(cutia_hr_time_size)
# desenha o grafico com a distribuicao de distancias perpendiculares
cutia_esec_terra_meio |>
filter(distance >= 1,
distance < 15) |>
plotar_distribuicao_distancia_interativo(largura_caixa = 1)
Warning: Continuous y aesthetic
ℹ did you forget `aes(group = ...)`?
Ajustando um modelo ao dados das cutias Dasyprocta
croconota, configurando uma distância limite de 20m e usando
Half-normal como key function usando o argumento
key, sem termo de ajuste.
cutia_esec_terra_meio_filtrado
cutia_esec_terra_meio_filtrado <- cutia_esec_terra_meio |>
filter(distance >= 1,
distance < 15)
# ajustando a função de detecção para uma distancia de truncamento de 20, 15, 10 e 5 metros
# dsitancias de truncamento
dist_truncamento <- list(
#`20 metros` = 20,
`15 metros` = 15,
`12 metros` = 12,
`10 metros` = 10
)
# Key function - Half-normal
cutia_esec_terra_meio_hn <- purrr::map(
dist_truncamento,
\(.x) ajuste_modelos_distance_hn(
cutia_esec_terra_meio_filtrado,
truncamento = .x
)
)
Fitting half-normal key function
AIC= 937.833
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 937.833
Fitting half-normal key function with cosine(2) adjustments
AIC= 936.52
Fitting half-normal key function with cosine(2,3) adjustments
Warning: Detection function is not strictly monotonic!AIC= 933.054
Fitting half-normal key function with cosine(2,3,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 932.305
Fitting half-normal key function with cosine(2,3,4,5) adjustments
Warning: Detection function is not strictly monotonic!AIC= 947.384
Half-normal key function with cosine(2,3,4) adjustments selected.
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 937.833
Fitting half-normal key function with Hermite(4) adjustments
AIC= 938.647
Half-normal key function selected.
Fitting half-normal key function
AIC= 807.11
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 807.11
Fitting half-normal key function with cosine(2) adjustments
Warning: First partial hessian is singular; using second-partial hessian
Warning: Detection function is not weakly monotonic!Warning: Detection function is not strictly monotonic!Warning: Detection function is greater than 1 at some distancesWarning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Half-normal key function selected.
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 807.11
Fitting half-normal key function with Hermite(4) adjustments
Warning: First partial hessian is singular; using second-partial hessian
AIC= 809.11
Half-normal key function selected.
Fitting half-normal key function
AIC= 632.908
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 632.908
Fitting half-normal key function with cosine(2) adjustments
Warning: First partial hessian is singular; using second-partial hessian
Warning: Detection function is not weakly monotonic!Warning: Detection function is not strictly monotonic!Warning: Detection function is greater than 1 at some distancesWarning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Half-normal key function selected.
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 632.908
Fitting half-normal key function with Hermite(4) adjustments
AIC= 634.908
Half-normal key function selected.
cutia_esec_terra_meio_hn
Ajustando um modelo ao dados da cutia Dasyprocta croconota,
configurando uma distância limite de 20m e usando Hazard rate
como key function usando o argumento key.
# ajustando a função de detecção para uma distancia de truncamento de 20, 15, 10 e 5 metros
# Key function - Hazard-rate
cutia_esec_terra_meio_hr <- purrr::map(
dist_truncamento,
\(.x) ajuste_modelos_distance_hr(
cutia_esec_terra_meio_filtrado,
truncamento = .x
)
)
Fitting hazard-rate key function
AIC= 925.724
Starting AIC adjustment term selection.
Fitting hazard-rate key function
AIC= 925.724
Fitting hazard-rate key function with cosine(2) adjustments
AIC= 927.806
Hazard-rate key function selected.
Starting AIC adjustment term selection.
Fitting hazard-rate key function
AIC= 925.724
Fitting hazard-rate key function with simple polynomial(4) adjustments
AIC= 927.806
Hazard-rate key function selected.
Fitting hazard-rate key function
Warning: First partial hessian is singular; using second-partial hessian
AIC= 808.565
Starting AIC adjustment term selection.
Fitting hazard-rate key function
Warning: First partial hessian is singular; using second-partial hessian
AIC= 808.565
Fitting hazard-rate key function with cosine(2) adjustments
Warning: First partial hessian is singular; using second-partial hessian
Warning: Detection function is not weakly monotonic!Warning: Detection function is not strictly monotonic!Warning: Detection function is greater than 1 at some distancesWarning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Hazard-rate key function selected.
Starting AIC adjustment term selection.
Fitting hazard-rate key function
Warning: First partial hessian is singular; using second-partial hessian
AIC= 808.565
Fitting hazard-rate key function with simple polynomial(4) adjustments
Warning: First partial hessian is singular; using second-partial hessian
AIC= 811.11
Hazard-rate key function selected.
Fitting hazard-rate key function
Warning: First partial hessian is singular; using second-partial hessian
AIC= 634.448
Starting AIC adjustment term selection.
Fitting hazard-rate key function
Warning: First partial hessian is singular; using second-partial hessian
AIC= 634.448
Fitting hazard-rate key function with cosine(2) adjustments
AIC= 636.958
Hazard-rate key function selected.
Starting AIC adjustment term selection.
Fitting hazard-rate key function
Warning: First partial hessian is singular; using second-partial hessian
AIC= 634.448
Fitting hazard-rate key function with simple polynomial(4) adjustments
Warning: First partial hessian is singular; using second-partial hessian
AIC= 636.908
Hazard-rate key function selected.
Ajustando um modelo ao dados das cutias Dasyprocta
croconota, configurando uma distância limite de 20m e usando
Uniform como key function usando o argumento
key, sem termo de ajuste.
# ajustando a função de detecção para uma distancia de truncamento de 20, 15, 10 e 5 metros
# Key function - Uniform
cutia_esec_terra_meio_unif <- purrr::map(
dist_truncamento,
\(.x) ajuste_modelos_distance_unif(
cutia_tap_arap,
truncamento = .x
)
)
Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 6477.656
Fitting uniform key function with cosine(1) adjustments
AIC= 6288.798
Fitting uniform key function with cosine(1,2) adjustments
AIC= 6280.568
Fitting uniform key function with cosine(1,2,3) adjustments
Warning: Detection function is not strictly monotonic!AIC= 6248.532
Fitting uniform key function with cosine(1,2,3,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 6238.947
Fitting uniform key function with cosine(1,2,3,4,5) adjustments
Warning: Detection function is not strictly monotonic!AIC= 6210.006
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 6477.656
Fitting uniform key function with simple polynomial(2) adjustments
AIC= 6345.058
Fitting uniform key function with simple polynomial(2,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 6290.986
Fitting uniform key function with simple polynomial(2,4,6) adjustments
Warning: Detection function is not strictly monotonic!AIC= 6288.551
Fitting uniform key function with simple polynomial(2,4,6,8) adjustments
Warning: Detection function is not strictly monotonic!AIC= 6288.125
Fitting uniform key function with simple polynomial(2,4,6,8,10) adjustments
Warning: Detection function is not strictly monotonic!AIC= 6284.527
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 5432.006
Fitting uniform key function with cosine(1) adjustments
AIC= 5340.635
Fitting uniform key function with cosine(1,2) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5319.03
Fitting uniform key function with cosine(1,2,3) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5282.176
Fitting uniform key function with cosine(1,2,3,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5271.725
Fitting uniform key function with cosine(1,2,3,4,5) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5213.714
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 5432.006
Fitting uniform key function with simple polynomial(2) adjustments
AIC= 5366.091
Fitting uniform key function with simple polynomial(2,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5346.537
Fitting uniform key function with simple polynomial(2,4,6) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5338.476
Fitting uniform key function with simple polynomial(2,4,6,8) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5332.686
Fitting uniform key function with simple polynomial(2,4,6,8,10) adjustments
Warning: Detection function is not strictly monotonic!AIC= 5326.193
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 4688.063
Fitting uniform key function with cosine(1) adjustments
AIC= 4647.342
Fitting uniform key function with cosine(1,2) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4617.841
Fitting uniform key function with cosine(1,2,3) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4579.748
Fitting uniform key function with cosine(1,2,3,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4551.3
Fitting uniform key function with cosine(1,2,3,4,5) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4523.924
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 4688.063
Fitting uniform key function with simple polynomial(2) adjustments
AIC= 4680.313
Fitting uniform key function with simple polynomial(2,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4651.715
Fitting uniform key function with simple polynomial(2,4,6) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4637.792
Fitting uniform key function with simple polynomial(2,4,6,8) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4631.554
Fitting uniform key function with simple polynomial(2,4,6,8,10) adjustments
Warning: Detection function is not strictly monotonic!AIC= 4622.947
Warning: Detection function is not strictly monotonic!
summarize_ds_models(
cutia_esec_terra_meio_hn$`20 metros`$`Sem termo`,
cutia_esec_terra_meio_hn$`20 metros`$Cosseno,
cutia_esec_terra_meio_hn$`20 metros`$`Hermite polinomial`,
cutia_esec_terra_meio_hr$`20 metros`$`Sem termo`,
cutia_esec_terra_meio_hr$`20 metros`$Cosseno,
cutia_esec_terra_meio_hr$`20 metros`$`Polinomial simples`,
cutia_esec_terra_meio_unif$`20 metros`$Cosseno,
cutia_esec_terra_meio_unif$`20 metros`$`Polinomial simples`
)
Warning: argumento não é numérico nem lógico: retornando NAWarning: argumento não é numérico nem lógico: retornando NAError in !binned : argumento de tipo inválido
summarize_ds_models(
cutia_esec_terra_meio_hn$`15 metros`$`Sem termo`,
cutia_esec_terra_meio_hn$`15 metros`$Cosseno,
cutia_esec_terra_meio_hn$`15 metros`$`Hermite polinomial`,
cutia_esec_terra_meio_hr$`15 metros`$`Sem termo`,
cutia_esec_terra_meio_hr$`15 metros`$Cosseno,
cutia_esec_terra_meio_hr$`15 metros`$`Polinomial simples`,
cutia_esec_terra_meio_unif$`15 metros`$Cosseno,
cutia_esec_terra_meio_unif$`15 metros`$`Polinomial simples`
)
summarize_ds_models(
cutia_esec_terra_meio_hn$`10 metros`$`Sem termo`,
cutia_esec_terra_meio_hn$`10 metros`$Cosseno,
cutia_esec_terra_meio_hn$`10 metros`$`Hermite polinomial`,
cutia_esec_terra_meio_hr$`10 metros`$`Sem termo`,
cutia_esec_terra_meio_hr$`10 metros`$Cosseno,
cutia_esec_terra_meio_hr$`10 metros`$`Polinomial simples`,
cutia_esec_terra_meio_unif$`10 metros`$Cosseno,
cutia_esec_terra_meio_unif$`10 metros`$`Polinomial simples`
)
summarize_ds_models(
cutia_esec_terra_meio_hn$`12 metros`$`Sem termo`,
cutia_esec_terra_meio_hn$`12 metros`$Cosseno,
cutia_esec_terra_meio_hn$`12 metros`$`Hermite polinomial`,
cutia_esec_terra_meio_hr$`12 metros`$`Sem termo`,
cutia_esec_terra_meio_hr$`12 metros`$Cosseno,
cutia_esec_terra_meio_hr$`12 metros`$`Polinomial simples`,
cutia_esec_terra_meio_unif$`12 metros`$Cosseno,
cutia_esec_terra_meio_unif$`12 metros`$`Polinomial simples`
)
cutia_parna_serra_pardo <- transformar_para_distanceR_covariaveis() |>
filter(
Region.Label == "Parna da Serra do Pardo",
sp_name == "Dasyprocta croconota"
) |>
drop_na(distance)
# desenha o grafico com a distribuicao de distancias perpendiculares
cutia_parna_serra_pardo |>
filter(distance < 15,
distance > 0) |>
plotar_distribuicao_distancia_interativo()
Warning: Continuous y aesthetic
ℹ did you forget `aes(group = ...)`?
Ajustando um modelo ao dados das cutias Dasyprocta
croconota, configurando uma distância limite de 20m e usando
Half-normal como key function usando o argumento
key, sem termo de ajuste.
# ajustando a função de detecção para uma distancia de truncamento de 20, 15, 10 e 5 metros
# dsitancias de truncamento
dist_truncamento <- list(
`20 metros` = 20,
`15 metros` = 15,
`10 metros` = 10,
`5 metros` = 5
)
# Key function - Half-normal
cutia_parna_serra_pardo_hn <- purrr::map(
dist_truncamento,
\(.x) ajuste_modelos_distance_hn(
cutia_parna_serra_pardo,
truncamento = .x
)
)
Fitting half-normal key function
AIC= 1400.725
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 1400.725
Fitting half-normal key function with cosine(2) adjustments
AIC= 1402.141
Half-normal key function selected.
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 1400.725
Fitting half-normal key function with Hermite(4) adjustments
AIC= 1402.044
Half-normal key function selected.
Fitting half-normal key function
AIC= 1315.127
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 1315.127
Fitting half-normal key function with cosine(2) adjustments
AIC= 1315.224
Half-normal key function selected.
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 1315.127
Fitting half-normal key function with Hermite(4) adjustments
AIC= 1317.126
Half-normal key function selected.
Fitting half-normal key function
AIC= 943.522
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 943.522
Fitting half-normal key function with cosine(2) adjustments
AIC= 934.647
Fitting half-normal key function with cosine(2,3) adjustments
Warning: Detection function is not strictly monotonic!AIC= 934.508
Fitting half-normal key function with cosine(2,3,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 947.489
Half-normal key function with cosine(2,3) adjustments selected.
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 943.522
Fitting half-normal key function with Hermite(4) adjustments
AIC= 945.333
Half-normal key function selected.
Fitting half-normal key function
AIC= 397.926
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 397.926
Fitting half-normal key function with cosine(2) adjustments
Warning: Detection function is not strictly monotonic!AIC= 370.676
Fitting half-normal key function with cosine(2,3) adjustments
Warning: Detection function is not strictly monotonic!AIC= 366.578
Fitting half-normal key function with cosine(2,3,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 837.783
Half-normal key function with cosine(2,3) adjustments selected.
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 397.926
Fitting half-normal key function with Hermite(4) adjustments
AIC= 399.683
Half-normal key function selected.
cutia_parna_serra_pardo_hn
$`20 metros`
$`20 metros`$`Sem termo`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 519.0546
$`20 metros`$Cosseno
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 519.0546
$`20 metros`$`Hermite polinomial`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 519.0546
$`15 metros`
$`15 metros`$`Sem termo`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 368.4514
$`15 metros`$Cosseno
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 368.4514
$`15 metros`$`Hermite polinomial`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 368.4514
$`10 metros`
$`10 metros`$`Sem termo`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 231.447
$`10 metros`$Cosseno
Distance sampling analysis object
Detection function:
Half-normal key function with cosine adjustment terms of order 2,3
Estimated abundance in covered region: 319.3262
$`10 metros`$`Hermite polinomial`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 231.447
$`5 metros`
$`5 metros`$`Sem termo`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 147.5382
$`5 metros`$Cosseno
Distance sampling analysis object
Detection function:
Half-normal key function with cosine adjustment terms of order 2,3
Estimated abundance in covered region: 264.7876
$`5 metros`$`Hermite polinomial`
Distance sampling analysis object
Detection function:
Half-normal key function
Estimated abundance in covered region: 147.5382
Ajustando um modelo ao dados da cutia Dasyprocta croconota,
configurando uma distância limite de 20m e usando Hazard rate
como key function usando o argumento key.
# ajustando a função de detecção para uma distancia de truncamento de 20, 15, 10 e 5 metros
# Key function - Hazard-rate
cutia_parna_serra_pardo_hr <- purrr::map(
dist_truncamento,
\(.x) ajuste_modelos_distance_hr(
cutia_parna_serra_pardo,
truncamento = .x
)
)
Fitting hazard-rate key function
AIC= 1402.11
Starting AIC adjustment term selection.
Fitting hazard-rate key function
AIC= 1402.11
Fitting hazard-rate key function with cosine(2) adjustments
AIC= 1396.291
Fitting hazard-rate key function with cosine(2,3) adjustments
Warning: Detection function is not strictly monotonic!AIC= 1393.787
Fitting hazard-rate key function with cosine(2,3,4) adjustments
Warning: Detection function is not weakly monotonic!Warning: Detection function is not strictly monotonic!Warning: Detection function is greater than 1 at some distancesWarning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Hazard-rate key function with cosine(2,3) adjustments selected.
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting hazard-rate key function
AIC= 1402.11
Fitting hazard-rate key function with simple polynomial(4) adjustments
AIC= 1408.986
Hazard-rate key function selected.
Fitting hazard-rate key function
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).AIC= 1252.293
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).Starting AIC adjustment term selection.
Fitting hazard-rate key function
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).AIC= 1252.293
Fitting hazard-rate key function with cosine(2) adjustments
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).Warning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Hazard-rate key function selected.
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).Starting AIC adjustment term selection.
Fitting hazard-rate key function
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).AIC= 1252.293
Fitting hazard-rate key function with simple polynomial(4) adjustments
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).Warning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Hazard-rate key function selected.
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).Fitting hazard-rate key function
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).AIC= 821.762
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).Starting AIC adjustment term selection.
Fitting hazard-rate key function
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).AIC= 821.762
Fitting hazard-rate key function with cosine(2) adjustments
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).Warning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Hazard-rate key function selected.
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).Starting AIC adjustment term selection.
Fitting hazard-rate key function
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).AIC= 821.762
Fitting hazard-rate key function with simple polynomial(4) adjustments
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).Warning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Hazard-rate key function selected.
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).Fitting hazard-rate key function
AIC= 76.172
Starting AIC adjustment term selection.
Fitting hazard-rate key function
AIC= 76.172
Fitting hazard-rate key function with cosine(2) adjustments
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).Warning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Hazard-rate key function selected.
Starting AIC adjustment term selection.
Fitting hazard-rate key function
AIC= 76.172
Fitting hazard-rate key function with simple polynomial(4) adjustments
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).Warning: Model fitting did not converge. Try different initial values or different model
Model failed to converge.
Hazard-rate key function selected.
Ajustando um modelo ao dados das cutias Dasyprocta
croconota, configurando uma distância limite de 20m e usando
Uniform como key function usando o argumento
key, sem termo de ajuste.
# ajustando a função de detecção para uma distancia de truncamento de 20, 15, 10 e 5 metros
# Key function - Uniform
cutia_parna_serra_pardo_unif <- purrr::map(
dist_truncamento,
\(.x) ajuste_modelos_distance_unif(
cutia_parna_serra_pardo,
truncamento = .x
)
)
Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 1527.823
Fitting uniform key function with cosine(1) adjustments
AIC= 1397.715
Fitting uniform key function with cosine(1,2) adjustments
AIC= 1399.656
Uniform key function with cosine(1) adjustments selected.
Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 1527.823
Fitting uniform key function with simple polynomial(2) adjustments
AIC= 1430.868
Fitting uniform key function with simple polynomial(2,4) adjustments
AIC= 1400.747
Fitting uniform key function with simple polynomial(2,4,6) adjustments
Warning: Detection function is not strictly monotonic!AIC= 1399.677
Fitting uniform key function with simple polynomial(2,4,6,8) adjustments
Warning: Detection function is not strictly monotonic!AIC= 1401.389
Uniform key function with simple polynomial(2,4,6) adjustments selected.
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 1348.609
Fitting uniform key function with cosine(1) adjustments
AIC= 1316.18
Fitting uniform key function with cosine(1,2) adjustments
AIC= 1318.11
Uniform key function with cosine(1) adjustments selected.
Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 1348.609
Fitting uniform key function with simple polynomial(2) adjustments
AIC= 1316.568
Fitting uniform key function with simple polynomial(2,4) adjustments
AIC= 1317.168
Uniform key function with simple polynomial(2) adjustments selected.
Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 944.06
Fitting uniform key function with cosine(1) adjustments
AIC= 939.555
Fitting uniform key function with cosine(1,2) adjustments
Warning: Detection function is not strictly monotonic!AIC= 936.452
Fitting uniform key function with cosine(1,2,3) adjustments
Warning: Detection function is not strictly monotonic!AIC= 931.427
Fitting uniform key function with cosine(1,2,3,4) adjustments
Warning: Detection function is not strictly monotonic!AIC= 928.341
Fitting uniform key function with cosine(1,2,3,4,5) adjustments
Warning: Detection function is not strictly monotonic!AIC= 923.816
Warning: Detection function is not strictly monotonic!Starting AIC adjustment term selection.
Fitting uniform key function
AIC= 944.06
Fitting uniform key function with simple polynomial(2) adjustments
AIC= 944.093
Uniform key function selected.
Error in `purrr::map()`:
ℹ In index: 3.
ℹ With name: 10 metros.
Caused by error in `purrr::map()`:
ℹ In index: 2.
ℹ With name: Polinomial simples.
Caused by error in `t.default()`:
! argumento não é uma matriz
Backtrace:
1. purrr::map(...)
2. purrr:::map_("list", .x, .f, ..., .progress = .progress)
6. global .f(.x[[i]], ...)
7. global ajuste_modelos_distance_unif(cutia_parna_serra_pardo, truncamento = .x)
8. purrr::map(...)
9. purrr:::map_("list", .x, .f, ..., .progress = .progress)
13. .f(.x[[i]], ...)
14. Distance::ds(...)
15. mrds::dht(...)
18. base::t.default(clusters$vc$detection$partial)
summarize_ds_models(
cutia_parna_serra_pardo_hn$`20 metros`$`Sem termo`,
cutia_parna_serra_pardo_hn$`20 metros`$Cosseno,
cutia_parna_serra_pardo_hn$`20 metros`$`Hermite polinomial`,
cutia_parna_serra_pardo_hr$`20 metros`$`Sem termo`,
cutia_parna_serra_pardo_hr$`20 metros`$Cosseno,
cutia_parna_serra_pardo_hr$`20 metros`$`Polinomial simples`
)
summarize_ds_models(
cutia_parna_serra_pardo_hn$`15 metros`$`Sem termo`,
cutia_parna_serra_pardo_hn$`15 metros`$Cosseno,
cutia_parna_serra_pardo_hn$`15 metros`$`Hermite polinomial`,
cutia_parna_serra_pardo_hr$`15 metros`$`Sem termo`,
cutia_parna_serra_pardo_hr$`15 metros`$Cosseno,
cutia_parna_serra_pardo_hr$`15 metros`$`Polinomial simples`
)
summarize_ds_models(
cutia_parna_serra_pardo_hn$`10 metros`$`Sem termo`,
cutia_parna_serra_pardo_hn$`10 metros`$Cosseno,
cutia_parna_serra_pardo_hn$`10 metros`$`Hermite polinomial`,
cutia_parna_serra_pardo_hr$`10 metros`$`Sem termo`,
cutia_parna_serra_pardo_hr$`10 metros`$Cosseno,
cutia_parna_serra_pardo_hr$`10 metros`$`Polinomial simples`
)
summarize_ds_models(
cutia_parna_serra_pardo_hn$`5 metros`$`Sem termo`,
cutia_parna_serra_pardo_hn$`5 metros`$Cosseno,
cutia_parna_serra_pardo_hn$`5 metros`$`Hermite polinomial`,
cutia_parna_serra_pardo_hr$`5 metros`$`Sem termo`,
cutia_parna_serra_pardo_hr$`5 metros`$Cosseno,
cutia_parna_serra_pardo_hr$`5 metros`$`Polinomial simples`
)
purrr::map_df(
list(
cutia_esec_terra_meio_hn$`20 metros`,
cutia_esec_terra_meio_hr$`20 metros`
),
\(.x) purrr::map_df(.x, \(.y) summarize_ds_models(.y))
)
purrr::map_df(
cutia_esec_terra_meio_hn$`15 metros`,
\(.x) summarize_ds_models(.x)
)
purrr::map_df(
cutia_esec_terra_meio_hn$`10 metros`,
\(.x) summarize_ds_models(.x)
)
purrr::map_df(
cutia_esec_terra_meio_hn$`5 metros`,
\(.x) summarize_ds_models(.x)
)
sagui_mont_tumuc <- transformar_para_distanceR_covariaveis() |>
filter(
Region.Label == "Parna Montanhas do Tumucumaque",
sp_name == "Saguinus midas"
) |>
drop_na(distance)
sagui_mont_tumuc |>
plotar_distribuicao_distancia_interativo()
Warning: Continuous y aesthetic
ℹ did you forget `aes(group = ...)`?
sagui_mont_tumuc_hn <- sagui_mont_tumuc |>
ajuste_modelos_distance_hn(truncamento = 10)
Fitting half-normal key function
AIC= 353.849
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 353.849
Fitting half-normal key function with cosine(2) adjustments
AIC= 354.52
Half-normal key function selected.
Starting AIC adjustment term selection.
Fitting half-normal key function
AIC= 353.849
Fitting half-normal key function with Hermite(4) adjustments
AIC= 355.818
Half-normal key function selected.
sagui_mont_tumuc_hr <- sagui_mont_tumuc |>
ajuste_modelos_distance_hr(truncamento = 10)
Fitting hazard-rate key function
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).AIC= 355.417
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).Starting AIC adjustment term selection.
Fitting hazard-rate key function
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).AIC= 355.417
Fitting hazard-rate key function with cosine(2) adjustments
Warning: Detection function is not strictly monotonic!AIC= 356.358
Hazard-rate key function selected.
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).Starting AIC adjustment term selection.
Fitting hazard-rate key function
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).AIC= 355.417
Fitting hazard-rate key function with simple polynomial(4) adjustments
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).AIC= 360.38
Hazard-rate key function selected.
Warning: Estimated hazard-rate scale parameter close to 0 (on log scale). Possible problem in data (e.g., spike near zero distance).
sagui_mont_tumuc_hn |>
purrr::map(\(.x) plot(.x))
$`Sem termo`
NULL
$Cosseno
NULL
$`Hermite polinomial`
NULL
sagui_mont_tumuc_hr |>
purrr::map(\(.x) plot(.x))
$`Sem termo`
NULL
$Cosseno
NULL
$`Polinomial simples`
NULL
summarize_ds_models(
sagui_mont_tumuc_hn$`Sem termo`,
sagui_mont_tumuc_hn$Cosseno,
sagui_mont_tumuc_hn$`Hermite polinomial`,
sagui_mont_tumuc_hr$`Sem termo`,
sagui_mont_tumuc_hr$Cosseno,
sagui_mont_tumuc_hr$`Polinomial simples`
)
sagui_mont_tumuc_hn |>
purrr::map(\(.x) gof_ds(model = .x))
$`Sem termo`
Goodness of fit results for ddf object
Distance sampling Cramer-von Mises test (unweighted)
Test statistic = 0.301163 p-value = 0.134156
$Cosseno
Goodness of fit results for ddf object
Distance sampling Cramer-von Mises test (unweighted)
Test statistic = 0.301163 p-value = 0.134156
$`Hermite polinomial`
Goodness of fit results for ddf object
Distance sampling Cramer-von Mises test (unweighted)
Test statistic = 0.301163 p-value = 0.134156
sagui_mont_tumuc_hr |>
purrr::map(\(.x) gof_ds(model = .x))
$`Sem termo`
Goodness of fit results for ddf object
Distance sampling Cramer-von Mises test (unweighted)
Test statistic = 0.288212 p-value = 0.145969
$Cosseno
Goodness of fit results for ddf object
Distance sampling Cramer-von Mises test (unweighted)
Test statistic = 0.288212 p-value = 0.145969
$`Polinomial simples`
Goodness of fit results for ddf object
Distance sampling Cramer-von Mises test (unweighted)
Test statistic = 0.288212 p-value = 0.145969
(125.8 + 67.6 + 240.8)/4e+07
[1] 1.0855e-05
dados_entre_60_130_aninhado_especie <- dados_entre_60_130 |>
nest(sp_name)